KNN Classification with One-step Computation

Associative Classification with Knn

Associative classification usually generates a large set of rules. Therefore, it is inevitable that an instance matches several rules which classes are conflicted. In this paper, a new framework called Associative Classification with KNN (AC-KNN) is proposed, which uses an improved KNN algorithm to address rule conflicts. Traditional K-Nearest Neighbor (KNN) is low efficient due to its calculat...

KNN-CF Approach: Incorporating Certainty Factor to kNN Classification

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distr...

One step forward

One Step Closer towards a Realistic Model for Parallel Computation

We present a new model of parallel computation|the LogGP model|and use it to analyze a number of algorithms, most notably, the single node scatter (one-to-all personalized broadcast). The LogGP model is an extension of the LogP model for parallel computation CKP + 93] which abstracts the communication of xed-sized short messages through the use of four parameters: the communication latency (L),...

One Step Forward: Periodic Computation in Dynamical Systems

The nonlinear dynamics of chaotic circuits generate a rich spectrum of signals. This observation suggests that these circuits could potentially provide an implementation of a novel framework of computation. In support of this hypothesis, computational complexity of the nervous system is achieved in large part through the nonlinear elements of electrically excitable membranes. In this study, we ...